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base_model: "answerdotai/ModernBERT-base"
HAV classifier (modernbert-base)
Binary: High Analytical Value (HAV) vs Low (LAV) for social posts. Precision-optimised (F0.5).
Data preparation
Model has been trained on data with spam removed, spam posts could be classified as HAV. Not trained on tiktok data.
Operating point
Apply a decision threshold of 0.909 on softmax(logits)[:, 1].
Do NOT use argmax@0.5 — this model is precision-tuned and 0.5 gives far lower precision.
Metrics
"objective": { "metric": "F0.5 (HAV)", "beta": 0.5, "min_hav_recall": 0.3, "decision_threshold": 0.9099, "val_selection": { "threshold": 0.9099, "hav_precision": 0.8238, "hav_recall": 0.5035, "nonhav_recall": 0.9753, "fbeta": 0.7308, "beta": 0.5 } }, "data": { "train_rows": 10866, "train_hav": 2033, "eval_rows": 2366, "eval_hav": 440 }, "test_overall": { "accuracy": 0.8762, "hav_f05": 0.6841, "hav_f1": 0.5936, "macro_f1": 0.7603, "weighted_f1": 0.865, "average_precision": 0.7163 }, "test_per_class": { "non_HAV": { "precision": 0.8916, "recall": 0.9652, "f1-score": 0.927, "support": 1926.0 }, "HAV": { "precision": 0.7616, "recall": 0.4864, "f1-score": 0.5936, "support": 440.0 } }
Caveats
Misclassfied negative class still an issue. pos_weight=4.9, max_length=256.
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